Page 277 - Integrated Wireless Propagation Models

M i c r o c e l l P r e d i c t i o n M o d e l s 255

where L, = The reflection path loss defined by
j(=) 4n
L, - -20 log 10 (x1 + x )+ x 1x 2 -- - 2 0 log [ ] dB (4.5.5.3.3.2)
_
2
"
w w z 10 �
J
where
j -41 + 110= for= :s; 0.33
-13.94+ 28= for 0.33 <= ::; 0.42
j(=) = -5.33 + 7.51 <X for 0.42 <= ::; 0.71 (4.5.5.3.3.3)
0 for= > 0.71

where Ld = The diffraction path loss defined by

( 4.5.5.3.3.4)

D � - [��][arc tan(�: ) + arc tan(��)-�] (4.5.5.3.3.5)
a

4.6 Summary
In this chapter, microcell models for both empirical and deterministic methods are dis­
cussed and compared. The exponential growth of wireless system demands accurate
and efficient propagation models. Although many researchers have been working hard
during the past few decades in the area of field strength prediction, numerous problems
remain to be solved. Many different prediction models have been proposed, and each
of these has advantages and disadvantages and can be applied only in particular cir­
cumstances. Also, the models depend to a great extent on the accuracy of building, ter­
rain, and morphology databases.
The trade-off between the accuracy of prediction and speed is critical for microcell
prediction. Microcells often have to be deployed very quickly, and the environment is
much more complicated as microcells are situated in dense urban areas. In the microcell
system, signal coverage is usually not a problem. Therefore, the major goal of microcell
prediction models is to solve the potential interference from other pico- (in-building),
micro-, and macrocells, making prediction a challenging task. Even with a very high
resolution databases, propagation prediction is still affected by many other factors, such
as street furniture (signs, lampposts, and so on) and by details of the antenna siting and
its interaction with neighboring cells. As we discussed earlier, terrain, tree, water, and
other morphologies have a great impact on the accuracy of the model as well.
The Lee microcell prediction model is a statistical model based on street layout with
buildings and empirical data to predict signal strength. The basic principles and algo­
rithms of the model are simple and easy to implement. Using the statistical approach, it
requires only 2D. The empirical data received on the streets include the loss due to the
rooftop diffraction phenomenon. Therefore, in the Lee model, the heights of buildings
need not be considered. The other physical models do consider the heights of the build­
ings, as shown in Sec. 4.5.3.
In Table 4.6.1, make comparisons in seven categories among selected models, some
of which have been described in Chap. 3. The table gives us a general understanding of